Sweep frequency microwave NDT for subsurface defect detection in GFRP

Zhang, Hong; Xu, Lu-Xiong; Wu, Ruizhi; Simm, Anthony

doi:10.1784/insi.2018.60.3.123

Cited by 8 publications

(4 citation statements)

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“…PCA was used to reduce the 101-time steps down to only three uncorrelated PCA components [46]. This dimensionality reduction for the features vector is required to speed up the processing time.…”

Section: Microwave Signal Processingmentioning

confidence: 99%

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Assessment of Defects under Insulation Using K-Medoids Clustering Algorithm-Based Microwave Nondestructive Testing

et al. 2022

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Composite insulations, such as ceramics, are commonly utilized in the turbine system as a thermal coating barrier to protect the metal substrate against high temperatures and pressure. The presence of delamination in the composite insulations may cause turbine failure, leading to a catastrophic accident. Thus, regular non-destructive testing is required to detect and evaluate insulation defects. Among the non-destructive testing techniques, the microwave technique has emerged as a promising method for assessing defects in ceramic coatings. Although the method is promising, microwave non-destructive testing suffers from poor spatial imaging, making the defect assessment challenging. In this paper, a novel technique based on microwave non-destructive testing with a k-medoids clustering algorithm for delamination detection is proposed. The representative ceramic coating sample is scanned using a Q-band open-ended rectangular waveguide with 101 frequency points that operated between 33 to 50 GHz. The measured data is transformed from the frequency domain to the time domain using an inverse fast Fourier transform. The principal component analysis is then used to reduce the dimensionality of 101 time steps into only 3 dominant attributes. The attributes of each inspected location are classified as defect or defect-free using the k-medoids clustering algorithm for accurately detecting and sizing the defects in the ceramic insulation. The results reported in this paper highlight the superiority of the k-medoids clustering algorithm in delamination detection, with an accuracy rate of 95.4%. This is a significant step forward compared to earlier approaches for identifying ceramic defects.

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Section: Microwave Signal Processingmentioning

confidence: 99%

“…The actual delamination defects are presented in the actual ground truth, as demonstrated in Figure 8. PCA was used to reduce the 101-time steps down to only three uncorrelated PCA components [46]. This dimensionality reduction for the features vector is required to speed up the processing time.…”

Section: Microwave Signal Processingmentioning

confidence: 99%

Assessment of Defects under Insulation Using K-Medoids Clustering Algorithm-Based Microwave Nondestructive Testing

et al. 2022

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“…The difference in these peaks has been used for imaging the inspected sample which greatly improves defect detection. Moreover, principal component analysis (PCA) is employed for partitioning the acquired signal into three main sources in [34], [35]. The partitioned sources refer to the GFRP surface, the inner layer of GFRP and back metal.…”

Section: Several Microwave Ndt Techniques Have Been Reported For Undementioning

confidence: 99%

Microwave Nondestructive Testing for Defect Detection in Composites Based on K-Means Clustering Algorithm

et al. 2021

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“…Furthermore, reflection and transmission properties are influenced by lift-off and the frequency of operation during inspection. The experimental setup for microwave NDT has been depicted in Figure 7 [31]. A waveguide probe is placed above sample under test with a specified lift-off.…”

Section: Microwave Ndt (Mndt)mentioning

confidence: 99%

Nondestructive Testing for Corrosion Evaluation of Metal under Coating

Zhang

Yang

et al. 2021

Journal of Sensors

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The use of steel has grown rapidly over the past decades. However, corrosion under coating detection still presents challenges for nondestructive testing (NDT) techniques. One of such challenges is the lift-off introduced by complex structures. Inaccessibility due to structure leads corrosion to be undetected, which can lead to catastrophic failure. Furthermore, lift-off effects reduce the sensitivities. The limitations of existing NDT techniques heighten the need for novel approaches to the characterization of corrosion. This paper begins with a discussion of the challenges associated with corrosion detection of metal under coating. Secondly, reviews are given of the most NDT methods used for the detection of corrosion under coating. The different techniques based on nondestructive testing methods such as ultrasonic, acoustic, electromagnetic, radiographic, and thermographic have been detailed out. This review presents the significance and advantages provided by the emerging NDT techniques. In the end, the trends and identified problems are summarized.

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Sweep frequency microwave NDT for subsurface defect detection in GFRP

Cited by 8 publications

References 0 publications

Assessment of Defects under Insulation Using K-Medoids Clustering Algorithm-Based Microwave Nondestructive Testing

Assessment of Defects under Insulation Using K-Medoids Clustering Algorithm-Based Microwave Nondestructive Testing

Microwave Nondestructive Testing for Defect Detection in Composites Based on K-Means Clustering Algorithm

Nondestructive Testing for Corrosion Evaluation of Metal under Coating

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